Professor Jia Hu

Prof. Jia Hu is an Associate Professor in Computer Science at the University of Exeter. He received his PhD in Computer Science from the University of Bradford, UK, in 2010, and M.Eng. and B.Eng degrees in Electronic Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2006 and 2004, respectively.

His research interests include edge-cloud computing, resource optimization, applied machine learning, and network security. He has published over 150 research papers within these areas in prestigious international journals and magazines (e.g., IEEE TPDS, TC, TMC, JSAC, TWC, T-ITS, TVT, TII, IoT-J, COMMAG) and at reputable international conferences (e.g., Globecom, ICC). He serves on the editorial board of Computers & Electrical Engineering (Elsevier) and Internet of Things and Cyber-Physical Systems (KeAi). He has been guest editor of many special issues in major international journals (e.g., IEEE IoT Journal, IEEE TCE, Computer Networks, Computer Communications). He has served as General Chair/Co-Chair of IEEE IUCC’21, CIT'15, IUCC'15, etc. Program Chair/Co-Chair of IEEE TrustCom'23, BigDataSE’21, ISPA'20, ScalCom’19, SmartCity’18, CYBCONF’17, etc.

His research has been funded by UK EPSRC, Royal Society, EU Horizon, UK BEIS, NSFC (China), and industry. He had received the Best Paper Award at IEEE SOSE'16 and IUCC'14. He received the Outstanding Service Award and Outstanding Leadership Award for contributions to several IEEE conferences such as BigDataSE’21, ScalCom’19, SmartCity’18 and CIT’15. He also received the recognition of significant contribution to EPSRC Peer Review (top 4% of College members). He has successfully supervised/co-supervised 12 PhD students to completion in the UK. He is currently supervising 7 PhD students. He is a Fellow of HEA (Higher Education Academy).

*** I am looking for motivated and creative PhD students to conduct research on cutting-edge topics including Federated Learning, Edge Computing, Network Digital Twin, IoT, and Blockchain. PhD funding opportunities (CSC-Exeter, EPSRC, etc): https://www.exeter.ac.uk/pg-research/money/phdfunding/

Main Projects:

EPSRC IAA, Efficient Federated Edge Learning for Large Language Models, £62,138, 1 Oct 2024 - 30 Sep 2025, PI.

EPSRC, SustainAIRA6G: Energy-Efficient Sustainable AI-driven Resource Allocation for 6G-empowered Edge-Fog-Cloud Continuum, £118,093, 24/07/24 - 31/03/25, Co-I.

EPSRC, New Horizons, No. EP/X019160/1, £251,872, Real-Time Federated Learning at the Wireless Edge via Algorithm-Hardware Co-Design (PI, 01/03/2023 - 30/11/2024)

Royal Society, IEC\NSFC\211460, International Exchanges 2021 Cost Share (NSFC), £11,880, Lightweight and Explainable Edge-AI for Efficient Predictive Maintenance in Smart Manufacturing (PI, 31/03/2022 - 30/03/2024)

Royal Society, IEC\NSFC\223528, International Exchanges 2022 Cost Share (NSFC), £11,990, Intelligent Satellite Remote Sensing for Real-Time Accurate Geological Hazard Analysis (Co-I, 31/03/2023 - 30/03/2025)

EU Horizon Europe, no. 101086159, €841,800, ASCENT: Autonomous Vehicular Edge Computing and Networking for Intelligent Transportation (Co-I, 01/03/2023 – 28/02/2027)

EU Horizon-2020, no. 101008297, €952,200, INITIATE: Intelligent and Sustainable Aerial-Terrestrial IoT Networks, (Co-I, 01/01/2022 – 31/12/2025)

UK BEIS, £15K, Smart Meter System based Internet of Things applications (Academic PI, Nov 2022 – Feb 2023)

EPSRC, DTP Studentship, £76,000, Learning-Based Networking and Caching Technology for Mobile Crowd Sensing (PI, 2018-2022)

EPSRC, First Grant, No. EP/M013936/1, EP/M013936/2, £115,020, Analysis and Optimization of Cache Resource Allocation for Energy-Efficient Information-Centric Networking (PI, 01/04/2015 - 30/06/2017)

Industry KTP, £198,800, Online anomaly prediction and prevention for reliable cloud services based on massive multidimensional metrics, (Co-I, 08/2019 – 08/2023).

Industrial KTP, £223,200, High-Performance Distributed Algorithms and Key Technologies for Processing SDN Big Data, (Co-I, 01/2017 – 10/2020).

Industrial KTP, £250,000, Intelligent Fault Detection and Fault Localization Based on Advanced Log Mining in the Network Big Data Era, (Co-I, 08/2016 – 10/2019).

National Natural Science Foundation of China (NSFC), No. 61972074, RMB 60k, "Research on Edge Computing Network Architecture and Key Technologies for Urban Internet of Things", (Co-I, Jan 2020 – Dec 2023)

National Natural Science Foundation of China (NSFC), No. 61772181, RMB 64k, " Research on the Key Technologies of Data Sovereign Security in Cloud Environment ", (Co-I, Jan 2018 - Dec 2021)

Selected Publications (last 5 years):

Z. Wang, J. Hu, G. Min, Z. Zhao, Z. Wang, Agile Cache Replacement in Edge Computing via Offline-Online Deep Reinforcement Learning, IEEE Transactions on Parallel and Distributed Systems, DOI: 10.1109/TPDS.2024.3368763, 2024.

Y. Zhang, J. Hu, G. Min, X. Chen, N. Georgalas, Joint Charging Scheduling and Computation Offloading in EV-Assisted Edge Computing: A Safe DRL Approach, IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2024.3355868, 2024.

Y. Zhang, J. Hu, G. Min, Digital Twin-Driven Intelligent Task Offloading for Collaborative Mobile Edge Computing, DOI: 10.1109/JSAC.2023.3310058, IEEE Journal on Selected Areas in Communications, 2023.

Z. Wang, J. Hu, G. Min, Z. Zhao, Intelligent Cooperative Caching at Mobile Edge based on Offline Deep Reinforcement Learning, DOI: 10.1145/3623398, ACM Transactions on Sensor Networks, 2023.

R. Jin, J. Hu, G. Min, et al., Lightweight Blockchain-empowered Secure and Efficient Federated Edge Learning, IEEE Transactions on Computers, DOI: 10.1109/TC.2023.3293731, 2023. (source code)

J. Mills, J. Hu, G. Min, Faster Federated Learning with Decaying Number of Local SGD Steps, IEEE Transactions on Parallel and Distributed Systems, DOI: 10.1109/TPDS.2023.3277367, 2023.

J. Wang, J. Hu, J. Mills, et al., Federated Ensemble Model-based Reinforcement Learning in Edge Computing, IEEE Transactions on Parallel and Distributed Systems, DOI: 10.1109/TPDS.2023.3264480, 2023.

J. Mills, J. Hu, G. Min, et al., Accelerating Federated Learning With a Global Biased Optimiser, IEEE Transactions on Computers, DOI: 10.1109/TC.2022.3212631, 2022. (source code)

J. Wang, J. Hu, G. Min, et al., Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach, IEEE Transactions on Mobile Computing, vol. 22, no.11, pp. 6663 - 6675, 2023. (source code)

J. Mills, J. Hu, G. Min, Client-Side Optimization Strategies for Communication-Efficient Federated Learning, IEEE Communications Magazine, vol. 60, no. 7, pp. 60 - 66, 2022.

Z. Wang, J. Hu, G. Min, et al., Spatial-Temporal Cellular Traffic Prediction for 5 G and Beyond: A Graph Neural Networks-Based Approach, IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2022.3182768, 2022.

R. Jin, J. Hu, G. Min, et al., Byzantine-Robust and Efficient Federated Learning for the Internet of Things, IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 114 - 118, 2022.

J. Mills, J. Hu, G. Min, Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing, IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 3, pp. 630-641, 2022. (ESI highly cited paper) (source code)

J. Wang, J. Hu, G. Min, et al., Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning, IEEE Transactions on Computers, doi:10.1109/TC.2021.3131040, 2021. (source code)

J. Wang, J. Hu, G. Min, et al., Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning, IEEE Transactions on Parallel and Distributed Systems, 32(1): 242 - 253, 2021. (ESI highly cited paper) (source code)

Z. Chen, J. Hu, G. Min, et al., Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning, IEEE Transactions on Parallel and Distributed Systems, DOI: 10.1109/TPDS.2021.3132422, 2021.

Z. Yu, J. Hu, G. Min, et al., Mobility-Aware Proactive Edge Caching for Connected Vehicles using Federated Learning, IEEE Transactions on Intelligent Transportation Systems, 22(8): 5341-5351, 2021. (ESI highly cited paper)

H. Lin, S. Garg, J. Hu, et al., Privacy-enhanced Data Fusion for COVID-19 Applications in Intelligent Internet of Medical Things, IEEE Internet of Things Journal, 8(21): 15683 - 15693, 2021. (ESI highly cited paper)

Z. Wang, J. Hu, G. Min, et al., Data Augmentation based Cellular Traffic Prediction in Edge Computing-Enabled Smart City, IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4179 - 4187, 2021.

J. Mills, J. Hu, G. Min, Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT, IEEE Internet of Things Journal, vol. 7, no. 7, 5986 - 5994, 2020. (ESI highly cited paper) (source code)

Z. Chen, J. Hu, G. Min, et al., Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning, IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 4, 923 - 934, 2020.

P. Liu, C. Wang, J. Hu, T. Fu, N. Cheng, N. Zhang, X. Shen, "Joint Route Selection and Charging Discharging Scheduling of EVs in V2G Energy Network," IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 10630-10641, 2020.

J. Wang, J. Hu, G. Min, W. Zhan, Q. Ni, N. Georgalas, Computation Offloading in Multi-Access Edge Computing Using a Deep Sequential Model Based on Reinforcement Learning, IEEE Communications Magazine, vol. 57, no. 5, pp. 64-69, 2019.

A full list of publications can be found at my Google scholar page: https://scholar.google.co.uk/citations?user=n1Q5NG4AAAAJ&hl=en&oi=ao